In [ ]:
import numpy as np # numpy is THE toolbox for scientific computing with python
import pandas as pd # pandas provides THE data structure and data analysis tools for data scientists 

# To plot pretty figures
%matplotlib inline
import matplotlib as mpl
import matplotlib.pyplot as plt
mpl.rc('axes', labelsize=14)
mpl.rc('xtick', labelsize=12)
mpl.rc('ytick', labelsize=12)

# seaborn plotting 
import seaborn as sns
In [ ]:
data = pd.read_csv("ai4i2020.csv")
In [ ]:
data.head()
Out[ ]:
UDI Product ID Type Air temperature [K] Process temperature [K] Rotational speed [rpm] Torque [Nm] Tool wear [min] Machine failure TWF HDF PWF OSF RNF
0 1 M14860 M 298.1 308.6 1551 42.8 0 0 0 0 0 0 0
1 2 L47181 L 298.2 308.7 1408 46.3 3 0 0 0 0 0 0
2 3 L47182 L 298.1 308.5 1498 49.4 5 0 0 0 0 0 0
3 4 L47183 L 298.2 308.6 1433 39.5 7 0 0 0 0 0 0
4 5 L47184 L 298.2 308.7 1408 40.0 9 0 0 0 0 0 0
In [ ]:
data.shape
Out[ ]:
(10000, 14)
In [ ]:
from ydata_profiling import ProfileReport

profile = ProfileReport(data, title="Pandas Profiling Report")
In [ ]:
profile

Summarize dataset:   0%|          | 0/5 [00:00<?, ?it/s]
Generate report structure:   0%|          | 0/1 [00:00<?, ?it/s]
Render HTML:   0%|          | 0/1 [00:00<?, ?it/s]
Out[ ]:




















In [ ]:






data.describe().T





















Out[ ]:












count
mean
std
min
25%
50%
75%
max






UDI
10000.0
5000.50000
2886.895680
1.0
2500.75
5000.5
7500.25
10000.0




Air temperature [K]
10000.0
300.00493
2.000259
295.3
298.30
300.1
301.50
304.5




Process temperature [K]
10000.0
310.00556
1.483734
305.7
308.80
310.1
311.10
313.8




Rotational speed [rpm]
10000.0
1538.77610
179.284096
1168.0
1423.00
1503.0
1612.00
2886.0




Torque [Nm]
10000.0
39.98691
9.968934
3.8
33.20
40.1
46.80
76.6




Tool wear [min]
10000.0
107.95100
63.654147
0.0
53.00
108.0
162.00
253.0




Machine failure
10000.0
0.03390
0.180981
0.0
0.00
0.0
0.00
1.0




TWF
10000.0
0.00460
0.067671
0.0
0.00
0.0
0.00
1.0




HDF
10000.0
0.01150
0.106625
0.0
0.00
0.0
0.00
1.0




PWF
10000.0
0.00950
0.097009
0.0
0.00
0.0
0.00
1.0




OSF
10000.0
0.00980
0.098514
0.0
0.00
0.0
0.00
1.0




RNF
10000.0
0.00190
0.043550
0.0
0.00
0.0
0.00
1.0


























In [ ]:






data.select_dtypes(include=['object']).describe().T





















Out[ ]:












count
unique
top
freq






Product ID
10000
10000
M14860
1




Type
10000
3
L
6000


























In [ ]:






data.columns





















Out[ ]:




Index(['UDI', 'Product ID', 'Type', 'Air temperature [K]',
       'Process temperature [K]', 'Rotational speed [rpm]', 'Torque [Nm]',
       'Tool wear [min]', 'Machine failure', 'TWF', 'HDF', 'PWF', 'OSF',
       'RNF'],
      dtype='object')




















In [ ]:






num_cols = ['Air temperature [K]','Process temperature [K]', 'Rotational speed [rpm]', 'Torque [Nm]', 'Tool wear [min]']
cat_cols = ['Type', 'Failure type']
label = 'Machine failure'





















In [ ]:






data['Air temperature [K]'][data['Air temperature [K]'].isna().astype(int)==1]





















Out[ ]:




Series([], Name: Air temperature [K], dtype: float64)




















In [ ]:






# Create a figure with a 3x2 grid of subplots
# plt.figure(figsize=(12, 12))

# Loop through the first column (assuming it's numerical) and create histograms
for i, col in enumerate(num_cols):
    plt.subplot(3, 2, i+1)
    sns.histplot(data, x=col, kde=True, alpha=0.2, color='red', bins=15)
    plt.title(col)  # Add title to each subplot

# # Add a suptitle for the entire figure
# plt.suptitle("Data Distributions", fontsize=15)

# # Adjust spacing between subplots
# plt.tight_layout()

# # Display the plot
# plt.show()

























c:\ProgramData\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
c:\ProgramData\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
c:\ProgramData\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
c:\ProgramData\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
c:\ProgramData\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):












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In [ ]:






plt.figure(figsize = (10, 7))
for i, col in enumerate(num_cols):
    plt.subplot(2,3, i+1)
    sns.rugplot(data, x = col, hue = label, height = 0.1)
    sns.boxplot(data, x = col, width = 0.25)
plt.suptitle("Data Distributions")
plt.tight_layout()
plt.show()

























c:\ProgramData\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
c:\ProgramData\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
c:\ProgramData\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
c:\ProgramData\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
c:\ProgramData\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):












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In [ ]:






plt.figure(figsize = (10, 7))
for i, col in enumerate(num_cols):
    plt.subplot(2,3, i+1)
    sns.boxplot(data, x = label, y = col, width = 0.5)
plt.suptitle("Data Distribution in Relation to Machine Failure")
plt.tight_layout()
plt.show()
























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In [ ]:






# plt.figure(figsize = (6,6))
sns.heatmap(data[num_cols].corr(), annot = True, fmt=".2f")  # , square = True, annot = True, cmap = 'Blues', linewidths = 0.5)
# plt.title("Heatmap Analysis")
# plt.show()





















Out[ ]:




<Axes: >











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In [ ]:






data[num_cols].corr()





















Out[ ]:












Air temperature [K]
Process temperature [K]
Rotational speed [rpm]
Torque [Nm]
Tool wear [min]






Air temperature [K]
1.000000
0.876107
0.022670
-0.013778
0.013853




Process temperature [K]
0.876107
1.000000
0.019277
-0.014061
0.013488




Rotational speed [rpm]
0.022670
0.019277
1.000000
-0.875027
0.000223




Torque [Nm]
-0.013778
-0.014061
-0.875027
1.000000
-0.003093




Tool wear [min]
0.013853
0.013488
0.000223
-0.003093
1.000000


























In [ ]:






data.plot.hexbin(x='Air temperature [K]', y='Process temperature [K]', gridsize=20, cmap='Purples', figsize = (5,4))
plt.title("Hexbin Plot Between Process Temperature and Air Temperature")
plt.show()
























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In [ ]:






data.plot.hexbin(x='Rotational speed [rpm]', y='Torque [Nm]', gridsize=30, cmap='Purples', figsize = (5,4))
plt.title("Hexbin Plot Between Torque and Rotational speed")
plt.show()
























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In [ ]:






type_machine_failure = data[['Type', 'Machine failure']].pivot_table(index = 'Type', columns='Machine failure', aggfunc= lambda x: len(x), margins = True)
print(type_machine_failure)
plt.figure(figsize=(6,6))
sns.heatmap(type_machine_failure, annot=True, fmt='g', cmap='Blues', cbar=False, linewidths=0.5)
plt.title("Type vs Machine Failure")
plt.show()

























Machine failure     0    1    All
Type                             
H                 982   21   1003
L                5765  235   6000
M                2914   83   2997
All              9661  339  10000












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